ICIP 2006, Atlanta, GA

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Paper Detail

Session:Face/Facial Expression Detection and Recognition
Time:Monday, October 9, 16:40 - 17:00
Presentation: Lecture
Topic: Biometrics: Human activity, gait, and gaze analysis
Authors: Dejan Arsic; Technical University Munich 
 Joachim Schenk; Technical University Munich 
 Bj√∂rn Schuller; Technical University Munich 
 Frank Wallhoff; Technical University Munich 
 Gerhard Rigoll; Technical University Munich 
Abstract: Video based analysis of a persons' mood or behavior is in general performed by interpreting various features observed on the body. Facial actions, such as speaking, yawning or laughing, are considered as key features. Dynamic changes within the face can be modeled with the well known HMM. Unfortunately even within one class examples can show a high variance, because of unknown start and end state or the length of a facial action. In this work we therefore perform a decomposition of those into so called submotions. These can be robustly recognized with HMM, applying selected points in the face and their geometrical distances. Additionally the first and second derivation of the distances is included. A sequence of submotions is then interpreted with a dictionary and dynamic programing, as the order may be crucial. Analysing the frequency of sequences shows the relevance of the submotions order. In an experimental section we show, that our novel submotion approach outperforms a standard HMM with the same set of features by nearly 30% absolute recognition rate.